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+Metadata-Version: 1.0
+Name: mock
+Version: 1.0.0
+Summary: A Python Mocking and Patching Library for Testing
+Home-page: http://www.voidspace.org.uk/python/mock/
+Author: Michael Foord
+Author-email: michael@voidspace.org.uk
+License: UNKNOWN
+Description: mock is a library for testing in Python. It allows you to replace parts of
+        your system under test with mock objects and make assertions about how they
+        have been used.
+        
+        mock is now part of the Python standard library, available as `unittest.mock <
+        http://docs.python.org/py3k/library/unittest.mock.html#module-unittest.mock>`_
+        in Python 3.3 onwards.
+        
+        mock provides a core `MagicMock` class removing the need to create a host of
+        stubs throughout your test suite. After performing an action, you can make
+        assertions about which methods / attributes were used and arguments they were
+        called with. You can also specify return values and set needed attributes in
+        the normal way.
+        
+        mock is tested on Python versions 2.4-2.7 and Python 3. mock is also tested
+        with the latest versions of Jython and pypy.
+        
+        The mock module also provides utility functions / objects to assist with
+        testing, particularly monkey patching.
+        
+        * `PDF documentation for 1.0 beta 1
+          <http://www.voidspace.org.uk/downloads/mock-1.0.0.pdf>`_
+        * `mock on google code (repository and issue tracker)
+          <http://code.google.com/p/mock/>`_
+        * `mock documentation
+          <http://www.voidspace.org.uk/python/mock/>`_
+        * `mock on PyPI <http://pypi.python.org/pypi/mock/>`_
+        * `Mailing list (testing-in-python@lists.idyll.org)
+          <http://lists.idyll.org/listinfo/testing-in-python>`_
+        
+        Mock is very easy to use and is designed for use with
+        `unittest <http://pypi.python.org/pypi/unittest2>`_. Mock is based on
+        the 'action -> assertion' pattern instead of 'record -> replay' used by many
+        mocking frameworks. See the `mock documentation`_ for full details.
+        
+        Mock objects create all attributes and methods as you access them and store
+        details of how they have been used. You can configure them, to specify return
+        values or limit what attributes are available, and then make assertions about
+        how they have been used::
+        
+            >>> from mock import Mock
+            >>> real = ProductionClass()
+            >>> real.method = Mock(return_value=3)
+            >>> real.method(3, 4, 5, key='value')
+            3
+            >>> real.method.assert_called_with(3, 4, 5, key='value')
+        
+        `side_effect` allows you to perform side effects, return different values or
+        raise an exception when a mock is called::
+        
+           >>> mock = Mock(side_effect=KeyError('foo'))
+           >>> mock()
+           Traceback (most recent call last):
+            ...
+           KeyError: 'foo'
+           >>> values = {'a': 1, 'b': 2, 'c': 3}
+           >>> def side_effect(arg):
+           ...     return values[arg]
+           ...
+           >>> mock.side_effect = side_effect
+           >>> mock('a'), mock('b'), mock('c')
+           (3, 2, 1)
+           >>> mock.side_effect = [5, 4, 3, 2, 1]
+           >>> mock(), mock(), mock()
+           (5, 4, 3)
+        
+        Mock has many other ways you can configure it and control its behaviour. For
+        example the `spec` argument configures the mock to take its specification from
+        another object. Attempting to access attributes or methods on the mock that
+        don't exist on the spec will fail with an `AttributeError`.
+        
+        The `patch` decorator / context manager makes it easy to mock classes or
+        objects in a module under test. The object you specify will be replaced with a
+        mock (or other object) during the test and restored when the test ends::
+        
+            >>> from mock import patch
+            >>> @patch('test_module.ClassName1')
+            ... @patch('test_module.ClassName2')
+            ... def test(MockClass2, MockClass1):
+            ...     test_module.ClassName1()
+            ...     test_module.ClassName2()
+        
+            ...     assert MockClass1.called
+            ...     assert MockClass2.called
+            ...
+            >>> test()
+        
+        .. note::
+        
+           When you nest patch decorators the mocks are passed in to the decorated
+           function in the same order they applied (the normal *python* order that
+           decorators are applied). This means from the bottom up, so in the example
+           above the mock for `test_module.ClassName2` is passed in first.
+        
+           With `patch` it matters that you patch objects in the namespace where they
+           are looked up. This is normally straightforward, but for a quick guide
+           read `where to patch
+           <http://www.voidspace.org.uk/python/mock/patch.html#where-to-patch>`_.
+        
+        As well as a decorator `patch` can be used as a context manager in a with
+        statement::
+        
+            >>> with patch.object(ProductionClass, 'method') as mock_method:
+            ...     mock_method.return_value = None
+            ...     real = ProductionClass()
+            ...     real.method(1, 2, 3)
+            ...
+            >>> mock_method.assert_called_once_with(1, 2, 3)
+        
+        There is also `patch.dict` for setting values in a dictionary just during the
+        scope of a test and restoring the dictionary to its original state when the
+        test ends::
+        
+           >>> foo = {'key': 'value'}
+           >>> original = foo.copy()
+           >>> with patch.dict(foo, {'newkey': 'newvalue'}, clear=True):
+           ...     assert foo == {'newkey': 'newvalue'}
+           ...
+           >>> assert foo == original
+        
+        Mock supports the mocking of Python magic methods. The easiest way of
+        using magic methods is with the `MagicMock` class. It allows you to do
+        things like::
+        
+            >>> from mock import MagicMock
+            >>> mock = MagicMock()
+            >>> mock.__str__.return_value = 'foobarbaz'
+            >>> str(mock)
+            'foobarbaz'
+            >>> mock.__str__.assert_called_once_with()
+        
+        Mock allows you to assign functions (or other Mock instances) to magic methods
+        and they will be called appropriately. The MagicMock class is just a Mock
+        variant that has all of the magic methods pre-created for you (well - all the
+        useful ones anyway).
+        
+        The following is an example of using magic methods with the ordinary Mock
+        class::
+        
+            >>> from mock import Mock
+            >>> mock = Mock()
+            >>> mock.__str__ = Mock(return_value = 'wheeeeee')
+            >>> str(mock)
+            'wheeeeee'
+        
+        For ensuring that the mock objects your tests use have the same api as the
+        objects they are replacing, you can use "auto-speccing". Auto-speccing can
+        be done through the `autospec` argument to patch, or the `create_autospec`
+        function. Auto-speccing creates mock objects that have the same attributes
+        and methods as the objects they are replacing, and any functions and methods
+        (including constructors) have the same call signature as the real object.
+        
+        This ensures that your mocks will fail in the same way as your production
+        code if they are used incorrectly::
+        
+           >>> from mock import create_autospec
+           >>> def function(a, b, c):
+           ...     pass
+           ...
+           >>> mock_function = create_autospec(function, return_value='fishy')
+           >>> mock_function(1, 2, 3)
+           'fishy'
+           >>> mock_function.assert_called_once_with(1, 2, 3)
+           >>> mock_function('wrong arguments')
+           Traceback (most recent call last):
+            ...
+           TypeError: <lambda>() takes exactly 3 arguments (1 given)
+        
+        `create_autospec` can also be used on classes, where it copies the signature of
+        the `__init__` method, and on callable objects where it copies the signature of
+        the `__call__` method.
+        
+        The distribution contains tests and documentation. The tests require
+        `unittest2 <http://pypi.python.org/pypi/unittest2>`_ to run.
+        
+        Docs from the in-development version of `mock` can be found at
+        `mock.readthedocs.org <http://mock.readthedocs.org>`_.
+        
+Keywords: testing,test,mock,mocking,unittest,patching,stubs,fakes,doubles
+Platform: UNKNOWN
+Classifier: Development Status :: 5 - Production/Stable
+Classifier: Environment :: Console
+Classifier: Intended Audience :: Developers
+Classifier: License :: OSI Approved :: BSD License
+Classifier: Programming Language :: Python
+Classifier: Programming Language :: Python :: 2
+Classifier: Programming Language :: Python :: 3
+Classifier: Programming Language :: Python :: 2.4
+Classifier: Programming Language :: Python :: 2.5
+Classifier: Programming Language :: Python :: 2.6
+Classifier: Programming Language :: Python :: 2.7
+Classifier: Programming Language :: Python :: 3.1
+Classifier: Programming Language :: Python :: 3.2
+Classifier: Programming Language :: Python :: Implementation :: CPython
+Classifier: Programming Language :: Python :: Implementation :: PyPy
+Classifier: Programming Language :: Python :: Implementation :: Jython
+Classifier: Operating System :: OS Independent
+Classifier: Topic :: Software Development :: Libraries
+Classifier: Topic :: Software Development :: Libraries :: Python Modules
+Classifier: Topic :: Software Development :: Testing